Dense point-cloud representation of a scene using monocular vision

Diskin, Yakov; Asari, Vijayan K.

doi:10.1117/1.jei.24.2.023003

Cited by 7 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [24] presents a 3-D reconstruction method based on the collection of multiple frames by a monocular camera: pixel to pixel comparison of images from different positions allows the reconstruction of a dense point-cloud representation of the scene. Similarly, ref.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithm proposed to estimate the presence or absence of vegetation through a monocular camera takes its cue from a 3-D camera strategy [24,25]: in the latter, the estimation distances come from a pair of images acquired simultaneously by two separate cameras placed at a known distance one from the other. The same strategy can, however, also be adopted using a single camera that is translated horizontally and acquires a couple of images in two different positions.…”

Section: System Based On Structure From Linear Motion (Sflm)mentioning

confidence: 99%

See 1 more Smart Citation

Structure from Linear Motion (SfLM): An On-the-Go Canopy Profiling System Based on Off-the-Shelf RGB Cameras for Effective Sprayers Control

et al. 2022

View full text Add to dashboard Cite

Phytosanitary treatment is one of the most critical operations in vineyard management. Ideally, the spraying system should treat only the canopy, avoiding drift, leakage and wasting of product where leaves are not present: variable rate distribution can be a successful approach, allowing the minimization of losses and improving economic as well as environmental performances. The target of this paper is to realize a smart control system to spray phytosanitary treatment just on the leaves, optimizing the overall costs/benefits ratio. Four different optical-based systems for leaf recognition are analyzed, and their performances are compared using a synthetic vineyard model. In the paper, we consider the usage of three well-established methods (infrared barriers, LIDAR 2-D and stereoscopic cameras), and we compare them with an innovative low-cost real-time solution based on a suitable computer vision algorithm that uses a simple monocular camera as input. The proposed algorithm, analyzing the sequence of input frames and exploiting the parallax property, estimates the depth map and eventually reconstructs the profile of the vineyard’s row to be treated. Finally, the performances obtained by the new method are evaluated and compared with those of the other methods on a well-controlled artificial environment resembling an actual vineyard setup while traveling at standard tractor forward speed.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: System Based On Structure From Linear Motion (Sflm)mentioning

confidence: 99%

Structure from Linear Motion (SfLM): An On-the-Go Canopy Profiling System Based on Off-the-Shelf RGB Cameras for Effective Sprayers Control

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For the case of landing site selection, the goal is to find a planar surface with a small slope that is free of obstacles, which can be conducted through assessing the landing risk by either constructing an elevation map or evaluating the planarity of terrain appearance. The former approach shall build a full three-dimensional (3-D) geometry 1 with the corresponding coordinates of the environment by means of a sequence of images, e.g., structure from motion (SFM). [2][3][4][5] As a consequence of estimated topographical information, a two-dimensional elevation map was thus obtained to comprehend the region flatness by, for instance, least median squares 6 or plane fit.…”

Section: Introductionmentioning

confidence: 99%

Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area

2019

View full text Add to dashboard Cite

We proposed a vision-based methodology as an aid for an unmanned aerial vehicle (UAV) landing on a previously unsurveyed area. When the UAV was commanded to perform a landing mission in an unknown airfield, the learning procedure was activated to extract the surface features for learning the obstacle appearance. After the learning process, while hovering the UAV above the potential landing spot, the vision system would be able to predict the roughness value for confidence in a safe landing. Finally, using hybrid optical flow technology for motion estimation, we successfully carried out the UAV landing without a predefined target. Our work combines a well-equipped flight control system with the proposed vision system to yield more practical versatility for UAV applications.

show abstract

“…with texture-rich images, in systems such as in Phototourism and recent large-scale 3D reconstuction work [15,47,34,6]. Despite their manifest usefulness, these methods generally cannot represent smooth, textureless regions (due to the sparsity of interest points in image regions with homogeneous appearance), or regions that change appearance drastically across views.…”

Section: Introductionmentioning

confidence: 99%

From Multiview Image Curves to 3D Drawings

Usumezbas

Fabbri

Kimia

2016

Computer Vision – ECCV 2016

View full text Add to dashboard Cite

Abstract. Reconstructing 3D scenes from multiple views has made impressive strides in recent years, chiefly by correlating isolated feature points, intensity patterns, or curvilinear structures. In the general setting -without controlled acquisition, abundant texture, curves and surfaces following specific models or limiting scene complexity -most methods produce unorganized point clouds, meshes, or voxel representations, with some exceptions producing unorganized clouds of 3D curve fragments. Ideally, many applications require structured representations of curves, surfaces and their spatial relationships. This paper presents a step in this direction by formulating an approach that combines 2D image curves into a collection of 3D curves, with topological connectivity between them represented as a 3D graph. This results in a 3D drawing, which is complementary to surface representations in the same sense as a 3D scaffold complements a tent taut over it. We evaluate our results against truth on synthetic and real datasets.

show abstract

Dense point-cloud representation of a scene using monocular vision

Cited by 7 publications

References 0 publications

Structure from Linear Motion (SfLM): An On-the-Go Canopy Profiling System Based on Off-the-Shelf RGB Cameras for Effective Sprayers Control

Structure from Linear Motion (SfLM): An On-the-Go Canopy Profiling System Based on Off-the-Shelf RGB Cameras for Effective Sprayers Control

Learning-based risk assessment and motion estimation by vision for unmanned aerial vehicle landing in an unvisited area

From Multiview Image Curves to 3D Drawings

Contact Info

Product

Resources

About